Working Theano configs

Here are the Theano configurations that I have tested and worked.
These were tested on Windows 10 64-Bit, and Windows 7 64-Bit.
(I will update when I test on other OS’s and setups)

With GPU support, on CUDA and cuDNN

In order to allow Theano to use the GPU, you need to be on a machine with a supported Nvidia GPU, and have the CUDA toolkit and cuDNN setup. I will cover how to setup CUDA on a different post.

 [global]  
 floatX = float32  
 device = gpu  
   
 [nvcc]  
 flags=-LC:UsersThimiraAnaconda3  
 compiler_bindir=C:Program Files (x86)Microsoft Visual Studio 12.0VCbin  
   
 [dnn]  
 enabled = True  
   
 [lib]  
 cnmem=0.75  
   
 [blas]   
 ldflags=-LC:Dev_Toolsopenblasbin -lopenblas  

device = gpu tells Theano to use the GPU instead of the CPU.
flags=-LC:UsersThimiraAnaconda3 point this to your Python installation (I’m using Anaconda Python)
compiler_bindir=C:Program Files (x86)Microsoft Visual Studio 12.0VCbin point this to the bin dir of your Visual Studio installation (Note: CUDA only worked with Visual Studio 2013 for me)
[dnn] enabled = True this enables cuDNN
cnmem=0.75 set the memory limit Theano can use of the GPU. Here it’s set to 75% of the GPU memory
ldflags=-LC:Dev_Toolsopenblasbin -lopenblas point to your OpenBLAS installation. Refer to my earlier post Getting Theano working with OpenBLAS on Windows

With only CPU support

Since not everyone have a compatible Nvidia GPU to have CUDA.

 [global]  
 floatX = float32  
 device = cpu  
   
 [blas]  
 ldflags=-LC:Dev_Toolsopenblasbin -lopenblas  

device = cpu tells Theano to use the CPU.
ldflags=-LC:Dev_Toolsopenblasbin -lopenblas point to your OpenBLAS installation. Refer to my earlier post Getting Theano working with OpenBLAS on Windows

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